The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Background: Post-partum depression (PPD) is a form of postnatal depression that affects mothers. Clinical manifestations usually appear within six months after delivery. Risk factors that influence the severity of post-partum depression are not fully known in the Iraqi population.
Objectives: We aim to evaluate the risk factors and identify potential predictors that may influence the symptom levels (severity) of post-partum depression among Iraqi women from Baghdad.
Subjects and Methods: The current study is cross-sectional, and we used the Edinburgh Postnatal Depression Scale (EPDS) and a cut-off value of 13 to differentiate patients into two those with lower symptom levels (LSL) and higher symptom levels (HSL). We also explored p

Background: Prematurity and its complications are the major causes of neonatal and infant morbidity and mortality. Although the cause of preterm labor is often unknown, numerous etiological risk factors may be implicated. To identify the risk factors that lead to prematurity and assess the neonatal outcomes that preterm neonates may develop. Methods: This case-control study was conducted at AL-Elwiya Pediatric Teaching Hospital, Baghdad, Iraq, from the 1st of June to the 31st of December 2019. A non-randomized sample of 700 neonates admitted to the neonatal care unit was included in this study and divided into two groups of preterm full-term neonates as the experimental and control groups, respectively (n=350 each). The same questionnaire w

The COVID-19 pandemic has had a huge influence on human lives all around the world. The virus spread quickly and impacted millions of individuals, resulting in a large number of hospitalizations and fatalities. The pandemic has also impacted economics, education, and social connections, among other aspects of life. Coronavirus-generated Computed Tomography (CT) scans have Regions of Interest (ROIs). The use of a modified U-Net model structure to categorize the region of interest at the pixel level is a promising strategy that may increase the accuracy of detecting COVID-19-associated anomalies in CT images. The suggested method seeks to detect and isolate ROIs in CT scans that show the existence of ground-glass opacity, which is fre

Background: Osteoporosis is one of the major public health problems from which more and more people in the world are suffering. There is evidence suggesting that osteoporosis knowledge is one contributor to osteoporosis preventive behavior. Aim of the Study: To assess the knowledge regarding osteoporosis risk factors, prevention, and management in women of reproductive ages. To identify any association between knowledge and studied factors.

BACKGROUND: Coronavirus current pandemic (COVID-19) is the striking subject worldwide hitting countries in an unexplained non-universal pattern. Bacillus Calmette–Guérin (BCG) vaccine was an adopted recent justification depending on its non-specific immune activation properties. Still the problem of post-vaccine short duration of protection needs to be solved. The same protective mechanism was identified in active or latent tuberculosis (TB). For each single patient of active TB, there are about nine cases of asymptomatic latent TB apparently normal individuals living within the community without restrictions carrying benefits of immune activation and involved in re-infection cycles in an excellent example of repeated immunity tr